Overview

Dataset statistics

Number of variables41
Number of observations11757
Missing cells164692
Missing cells (%)34.2%
Total size in memory3.7 MiB
Average record size in memory328.0 B

Variable types

Numeric12
Text25
Unsupported4

Alerts

score_text has 11757 (100.0%) missing valuesMissing
violent_recid has 11757 (100.0%) missing valuesMissing
days_b_screening_arrest has 742 (6.3%) missing valuesMissing
c_jail_in has 742 (6.3%) missing valuesMissing
c_jail_out has 742 (6.3%) missing valuesMissing
c_case_number has 742 (6.3%) missing valuesMissing
c_days_from_compas has 742 (6.3%) missing valuesMissing
c_arrest_date has 9899 (84.2%) missing valuesMissing
c_offense_date has 2600 (22.1%) missing valuesMissing
c_charge_degree has 742 (6.3%) missing valuesMissing
c_charge_desc has 749 (6.4%) missing valuesMissing
num_r_cases has 8054 (68.5%) missing valuesMissing
r_case_number has 8054 (68.5%) missing valuesMissing
r_charge_degree has 8054 (68.5%) missing valuesMissing
r_days_from_arrest has 9297 (79.1%) missing valuesMissing
r_offense_date has 8054 (68.5%) missing valuesMissing
r_charge_desc has 8114 (69.0%) missing valuesMissing
r_jail_in has 9297 (79.1%) missing valuesMissing
r_jail_out has 9297 (79.1%) missing valuesMissing
num_vr_cases has 11757 (100.0%) missing valuesMissing
vr_case_number has 10875 (92.5%) missing valuesMissing
vr_charge_degree has 10875 (92.5%) missing valuesMissing
vr_offense_date has 10875 (92.5%) missing valuesMissing
vr_charge_desc has 10875 (92.5%) missing valuesMissing
id has unique valuesUnique
score_text is an unsupported type, check if it needs cleaning or further analysisUnsupported
violent_recid is an unsupported type, check if it needs cleaning or further analysisUnsupported
days_b_screening_arrest is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_vr_cases is an unsupported type, check if it needs cleaning or further analysisUnsupported
juv_fel_count has 11334 (96.4%) zerosZeros
juv_misd_count has 11199 (95.3%) zerosZeros
juv_other_count has 11030 (93.8%) zerosZeros
priors_count has 4184 (35.6%) zerosZeros
c_days_from_compas has 1332 (11.3%) zerosZeros
is_recid has 7335 (62.4%) zerosZeros
r_days_from_arrest has 1779 (15.1%) zerosZeros
is_violent_recid has 10875 (92.5%) zerosZeros

Reproduction

Analysis started2024-02-26 23:19:01.122107
Analysis finished2024-02-26 23:19:01.738579
Duration0.62 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIQUE 

Distinct11757
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5879
Minimum1
Maximum11757
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2024-02-26T23:19:01.789954image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile588.8
Q12940
median5879
Q38818
95-th percentile11169.2
Maximum11757
Range11756
Interquartile range (IQR)5878

Descriptive statistics

Standard deviation3394.097892
Coefficient of variation (CV)0.5773257173
Kurtosis-1.2
Mean5879
Median Absolute Deviation (MAD)2939
Skewness0
Sum69119403
Variance11519900.5
MonotonicityStrictly increasing
2024-02-26T23:19:01.883203image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
7832 1
 
< 0.1%
7834 1
 
< 0.1%
7835 1
 
< 0.1%
7836 1
 
< 0.1%
7837 1
 
< 0.1%
7838 1
 
< 0.1%
7839 1
 
< 0.1%
7840 1
 
< 0.1%
7841 1
 
< 0.1%
Other values (11747) 11747
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
ValueCountFrequency (%)
11757 1
< 0.1%
11756 1
< 0.1%
11755 1
< 0.1%
11754 1
< 0.1%
11753 1
< 0.1%

name
Text

Distinct11584
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
2024-02-26T23:19:02.039614image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length29
Median length27
Mean length13.78429872
Min length6

Characters and Unicode

Total characters162062
Distinct characters32
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11429 ?
Unique (%)97.2%

Sample

1st rowmiguel hernandez
2nd rowmichael ryan
3rd rowkevon dixon
4th rowed philo
5th rowmarcu brown
ValueCountFrequency (%)
michael 265
 
1.1%
joseph 162
 
0.7%
christopher 162
 
0.7%
williams 147
 
0.6%
james 146
 
0.6%
john 138
 
0.6%
anthony 137
 
0.6%
robert 131
 
0.6%
brown 130
 
0.6%
johnson 122
 
0.5%
Other values (9508) 22010
93.5%
2024-02-26T23:19:02.271478image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 16572
 
10.2%
e 16091
 
9.9%
r 13131
 
8.1%
n 12376
 
7.6%
11793
 
7.3%
i 10512
 
6.5%
o 10296
 
6.4%
l 9393
 
5.8%
s 8721
 
5.4%
t 6579
 
4.1%
Other values (22) 46598
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 150116
92.6%
Space Separator 11793
 
7.3%
Dash Punctuation 147
 
0.1%
Other Punctuation 5
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 16572
11.0%
e 16091
 
10.7%
r 13131
 
8.7%
n 12376
 
8.2%
i 10512
 
7.0%
o 10296
 
6.9%
l 9393
 
6.3%
s 8721
 
5.8%
t 6579
 
4.4%
h 5416
 
3.6%
Other values (16) 41029
27.3%
Other Punctuation
ValueCountFrequency (%)
. 3
60.0%
, 1
 
20.0%
' 1
 
20.0%
Space Separator
ValueCountFrequency (%)
11793
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 147
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 150116
92.6%
Common 11946
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 16572
11.0%
e 16091
 
10.7%
r 13131
 
8.7%
n 12376
 
8.2%
i 10512
 
7.0%
o 10296
 
6.9%
l 9393
 
6.3%
s 8721
 
5.8%
t 6579
 
4.4%
h 5416
 
3.6%
Other values (16) 41029
27.3%
Common
ValueCountFrequency (%)
11793
98.7%
- 147
 
1.2%
. 3
 
< 0.1%
, 1
 
< 0.1%
` 1
 
< 0.1%
' 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162062
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 16572
 
10.2%
e 16091
 
9.9%
r 13131
 
8.1%
n 12376
 
7.6%
11793
 
7.3%
i 10512
 
6.5%
o 10296
 
6.4%
l 9393
 
5.8%
s 8721
 
5.4%
t 6579
 
4.1%
Other values (22) 46598
28.8%

first
Text

Distinct4058
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
2024-02-26T23:19:02.440654image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length13
Median length12
Mean length6.18048822
Min length2

Characters and Unicode

Total characters72664
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2985 ?
Unique (%)25.4%

Sample

1st rowmiguel
2nd rowmichael
3rd rowkevon
4th rowed
5th rowmarcu
ValueCountFrequency (%)
michael 264
 
2.2%
christopher 162
 
1.4%
anthony 132
 
1.1%
james 131
 
1.1%
john 130
 
1.1%
robert 128
 
1.1%
david 117
 
1.0%
joseph 106
 
0.9%
daniel 96
 
0.8%
kevin 80
 
0.7%
Other values (4049) 10413
88.6%
2024-02-26T23:19:02.672411image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 9017
12.4%
e 8123
11.2%
r 6402
 
8.8%
n 6348
 
8.7%
i 5470
 
7.5%
o 4260
 
5.9%
l 4257
 
5.9%
s 3473
 
4.8%
t 3204
 
4.4%
h 2998
 
4.1%
Other values (21) 19112
26.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 72647
> 99.9%
Dash Punctuation 12
 
< 0.1%
Space Separator 2
 
< 0.1%
Other Punctuation 2
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 9017
12.4%
e 8123
11.2%
r 6402
 
8.8%
n 6348
 
8.7%
i 5470
 
7.5%
o 4260
 
5.9%
l 4257
 
5.9%
s 3473
 
4.8%
t 3204
 
4.4%
h 2998
 
4.1%
Other values (16) 19095
26.3%
Other Punctuation
ValueCountFrequency (%)
' 1
50.0%
. 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 72647
> 99.9%
Common 17
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 9017
12.4%
e 8123
11.2%
r 6402
 
8.8%
n 6348
 
8.7%
i 5470
 
7.5%
o 4260
 
5.9%
l 4257
 
5.9%
s 3473
 
4.8%
t 3204
 
4.4%
h 2998
 
4.1%
Other values (16) 19095
26.3%
Common
ValueCountFrequency (%)
- 12
70.6%
2
 
11.8%
` 1
 
5.9%
' 1
 
5.9%
. 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 9017
12.4%
e 8123
11.2%
r 6402
 
8.8%
n 6348
 
8.7%
i 5470
 
7.5%
o 4260
 
5.9%
l 4257
 
5.9%
s 3473
 
4.8%
t 3204
 
4.4%
h 2998
 
4.1%
Other values (21) 19112
26.3%

last
Text

Distinct5921
Distinct (%)50.4%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
2024-02-26T23:19:02.956217image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length20
Median length18
Mean length6.603810496
Min length1

Characters and Unicode

Total characters77641
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4535 ?
Unique (%)38.6%

Sample

1st rowhernandez
2nd rowryan
3rd rowdixon
4th rowphilo
5th rowbrown
ValueCountFrequency (%)
williams 145
 
1.2%
brown 130
 
1.1%
johnson 120
 
1.0%
smith 112
 
0.9%
jones 95
 
0.8%
davis 68
 
0.6%
jackson 68
 
0.6%
joseph 56
 
0.5%
thomas 52
 
0.4%
robinson 52
 
0.4%
Other values (5907) 10893
92.4%
2024-02-26T23:19:03.239397image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 7968
 
10.3%
a 7555
 
9.7%
r 6729
 
8.7%
o 6036
 
7.8%
n 6028
 
7.8%
s 5248
 
6.8%
l 5136
 
6.6%
i 5042
 
6.5%
t 3375
 
4.3%
m 2800
 
3.6%
Other values (20) 21724
28.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 77469
99.8%
Dash Punctuation 135
 
0.2%
Space Separator 34
 
< 0.1%
Other Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7968
 
10.3%
a 7555
 
9.8%
r 6729
 
8.7%
o 6036
 
7.8%
n 6028
 
7.8%
s 5248
 
6.8%
l 5136
 
6.6%
i 5042
 
6.5%
t 3375
 
4.4%
m 2800
 
3.6%
Other values (16) 21552
27.8%
Other Punctuation
ValueCountFrequency (%)
. 2
66.7%
, 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 135
100.0%
Space Separator
ValueCountFrequency (%)
34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 77469
99.8%
Common 172
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7968
 
10.3%
a 7555
 
9.8%
r 6729
 
8.7%
o 6036
 
7.8%
n 6028
 
7.8%
s 5248
 
6.8%
l 5136
 
6.6%
i 5042
 
6.5%
t 3375
 
4.4%
m 2800
 
3.6%
Other values (16) 21552
27.8%
Common
ValueCountFrequency (%)
- 135
78.5%
34
 
19.8%
. 2
 
1.2%
, 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77641
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7968
 
10.3%
a 7555
 
9.7%
r 6729
 
8.7%
o 6036
 
7.8%
n 6028
 
7.8%
s 5248
 
6.8%
l 5136
 
6.6%
i 5042
 
6.5%
t 3375
 
4.3%
m 2800
 
3.6%
Other values (20) 21724
28.0%

sex
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
2024-02-26T23:19:03.302317image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.411839755
Min length4

Characters and Unicode

Total characters51870
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale
ValueCountFrequency (%)
male 9336
79.4%
female 2421
 
20.6%
2024-02-26T23:19:03.415655image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 14178
27.3%
a 11757
22.7%
l 11757
22.7%
M 9336
18.0%
F 2421
 
4.7%
m 2421
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 40113
77.3%
Uppercase Letter 11757
 
22.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14178
35.3%
a 11757
29.3%
l 11757
29.3%
m 2421
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
M 9336
79.4%
F 2421
 
20.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 51870
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14178
27.3%
a 11757
22.7%
l 11757
22.7%
M 9336
18.0%
F 2421
 
4.7%
m 2421
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 14178
27.3%
a 11757
22.7%
l 11757
22.7%
M 9336
18.0%
F 2421
 
4.7%
m 2421
 
4.7%

race
Text

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
2024-02-26T23:19:03.509508image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length16
Median length15
Mean length12.14323382
Min length5

Characters and Unicode

Total characters142768
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowCaucasian
3rd rowAfrican-American
4th rowAfrican-American
5th rowAfrican-American
ValueCountFrequency (%)
african-american 5813
49.3%
caucasian 4085
34.6%
hispanic 1100
 
9.3%
other 661
 
5.6%
asian 58
 
0.5%
native 40
 
0.3%
american 40
 
0.3%
2024-02-26T23:19:03.662212image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 25119
17.6%
i 18049
12.6%
n 16909
11.8%
c 16851
11.8%
r 12327
8.6%
A 11724
8.2%
e 6554
 
4.6%
m 5853
 
4.1%
- 5813
 
4.1%
f 5813
 
4.1%
Other values (11) 17756
12.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 119305
83.6%
Uppercase Letter 17610
 
12.3%
Dash Punctuation 5813
 
4.1%
Space Separator 40
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 25119
21.1%
i 18049
15.1%
n 16909
14.2%
c 16851
14.1%
r 12327
10.3%
e 6554
 
5.5%
m 5853
 
4.9%
f 5813
 
4.9%
s 5243
 
4.4%
u 4085
 
3.4%
Other values (4) 2502
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
A 11724
66.6%
C 4085
 
23.2%
H 1100
 
6.2%
O 661
 
3.8%
N 40
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 5813
100.0%
Space Separator
ValueCountFrequency (%)
40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 136915
95.9%
Common 5853
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 25119
18.3%
i 18049
13.2%
n 16909
12.3%
c 16851
12.3%
r 12327
9.0%
A 11724
8.6%
e 6554
 
4.8%
m 5853
 
4.3%
f 5813
 
4.2%
s 5243
 
3.8%
Other values (9) 12473
9.1%
Common
ValueCountFrequency (%)
- 5813
99.3%
40
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 142768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 25119
17.6%
i 18049
12.6%
n 16909
11.8%
c 16851
11.8%
r 12327
8.6%
A 11724
8.2%
e 6554
 
4.6%
m 5853
 
4.1%
- 5813
 
4.1%
f 5813
 
4.1%
Other values (11) 17756
12.4%

dob
Text

Distinct7800
Distinct (%)66.3%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
2024-02-26T23:19:03.893623image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters305682
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5064 ?
Unique (%)43.1%

Sample

1st row1947-04-18 00:00:00.000000
2nd row1985-02-06 00:00:00.000000
3rd row1982-01-22 00:00:00.000000
4th row1991-05-14 00:00:00.000000
5th row1993-01-21 00:00:00.000000
ValueCountFrequency (%)
00:00:00.000000 11757
50.0%
1989-10-14 6
 
< 0.1%
1984-07-06 6
 
< 0.1%
1986-01-03 6
 
< 0.1%
1990-05-02 6
 
< 0.1%
1989-09-27 6
 
< 0.1%
1991-08-12 6
 
< 0.1%
1988-04-15 6
 
< 0.1%
1992-10-15 6
 
< 0.1%
1994-01-24 6
 
< 0.1%
Other values (7791) 11703
49.8%
2024-02-26T23:19:04.189898image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 156566
51.2%
1 23537
 
7.7%
- 23514
 
7.7%
: 23514
 
7.7%
9 18280
 
6.0%
11757
 
3.8%
. 11757
 
3.8%
2 8004
 
2.6%
8 7287
 
2.4%
7 5242
 
1.7%
Other values (4) 16224
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 235140
76.9%
Other Punctuation 35271
 
11.5%
Dash Punctuation 23514
 
7.7%
Space Separator 11757
 
3.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 156566
66.6%
1 23537
 
10.0%
9 18280
 
7.8%
2 8004
 
3.4%
8 7287
 
3.1%
7 5242
 
2.2%
6 4743
 
2.0%
3 4001
 
1.7%
5 3986
 
1.7%
4 3494
 
1.5%
Other Punctuation
ValueCountFrequency (%)
: 23514
66.7%
. 11757
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 23514
100.0%
Space Separator
ValueCountFrequency (%)
11757
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 305682
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 156566
51.2%
1 23537
 
7.7%
- 23514
 
7.7%
: 23514
 
7.7%
9 18280
 
6.0%
11757
 
3.8%
. 11757
 
3.8%
2 8004
 
2.6%
8 7287
 
2.4%
7 5242
 
1.7%
Other values (4) 16224
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 305682
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 156566
51.2%
1 23537
 
7.7%
- 23514
 
7.7%
: 23514
 
7.7%
9 18280
 
6.0%
11757
 
3.8%
. 11757
 
3.8%
2 8004
 
2.6%
8 7287
 
2.4%
7 5242
 
1.7%
Other values (4) 16224
 
5.3%

age
Real number (ℝ)

Distinct66
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.14331887
Minimum18
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2024-02-26T23:19:04.270569image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile21
Q125
median32
Q343
95-th percentile58
Maximum96
Range78
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.02289379
Coefficient of variation (CV)0.3421103691
Kurtosis-0.00717830284
Mean35.14331887
Median Absolute Deviation (MAD)8
Skewness0.8528079539
Sum413180
Variance144.5499751
MonotonicityNot monotonic
2024-02-26T23:19:04.344392image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 540
 
4.6%
24 539
 
4.6%
25 521
 
4.4%
23 507
 
4.3%
27 506
 
4.3%
22 503
 
4.3%
21 500
 
4.3%
29 456
 
3.9%
30 441
 
3.8%
28 430
 
3.7%
Other values (56) 6814
58.0%
ValueCountFrequency (%)
18 7
 
0.1%
19 66
 
0.6%
20 318
2.7%
21 500
4.3%
22 503
4.3%
ValueCountFrequency (%)
96 1
< 0.1%
86 1
< 0.1%
83 2
< 0.1%
80 1
< 0.1%
79 1
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
2024-02-26T23:19:04.410681image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length15
Median length7
Mean length9.853108786
Min length7

Characters and Unicode

Total characters115843
Distinct characters14
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGreater than 45
2nd row25 - 45
3rd row25 - 45
4th rowLess than 25
5th rowLess than 25
ValueCountFrequency (%)
45 9317
26.4%
25 9089
25.8%
6649
18.9%
than 5108
14.5%
greater 2668
 
7.6%
less 2440
 
6.9%
2024-02-26T23:19:04.542697image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23514
20.3%
5 18406
15.9%
4 9317
 
8.0%
2 9089
 
7.8%
e 7776
 
6.7%
a 7776
 
6.7%
t 7776
 
6.7%
- 6649
 
5.7%
r 5336
 
4.6%
h 5108
 
4.4%
Other values (4) 15096
13.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 43760
37.8%
Decimal Number 36812
31.8%
Space Separator 23514
20.3%
Dash Punctuation 6649
 
5.7%
Uppercase Letter 5108
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7776
17.8%
a 7776
17.8%
t 7776
17.8%
r 5336
12.2%
h 5108
11.7%
n 5108
11.7%
s 4880
11.2%
Decimal Number
ValueCountFrequency (%)
5 18406
50.0%
4 9317
25.3%
2 9089
24.7%
Uppercase Letter
ValueCountFrequency (%)
G 2668
52.2%
L 2440
47.8%
Space Separator
ValueCountFrequency (%)
23514
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6649
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 66975
57.8%
Latin 48868
42.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7776
15.9%
a 7776
15.9%
t 7776
15.9%
r 5336
10.9%
h 5108
10.5%
n 5108
10.5%
s 4880
10.0%
G 2668
 
5.5%
L 2440
 
5.0%
Common
ValueCountFrequency (%)
23514
35.1%
5 18406
27.5%
4 9317
 
13.9%
2 9089
 
13.6%
- 6649
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 115843
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
23514
20.3%
5 18406
15.9%
4 9317
 
8.0%
2 9089
 
7.8%
e 7776
 
6.7%
a 7776
 
6.7%
t 7776
 
6.7%
- 6649
 
5.7%
r 5336
 
4.6%
h 5108
 
4.4%
Other values (4) 15096
13.0%

juv_fel_count
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06158033512
Minimum0
Maximum20
Zeros11334
Zeros (%)96.4%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2024-02-26T23:19:04.601979image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4453280822
Coefficient of variation (CV)7.231660584
Kurtosis504.6084819
Mean0.06158033512
Median Absolute Deviation (MAD)0
Skewness17.05335375
Sum724
Variance0.1983171008
MonotonicityNot monotonic
2024-02-26T23:19:04.660132image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 11334
96.4%
1 280
 
2.4%
2 80
 
0.7%
3 30
 
0.3%
4 16
 
0.1%
5 8
 
0.1%
8 2
 
< 0.1%
6 2
 
< 0.1%
10 2
 
< 0.1%
9 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0 11334
96.4%
1 280
 
2.4%
2 80
 
0.7%
3 30
 
0.3%
4 16
 
0.1%
ValueCountFrequency (%)
20 1
< 0.1%
13 1
< 0.1%
10 2
< 0.1%
9 1
< 0.1%
8 2
< 0.1%

juv_misd_count
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07603980607
Minimum0
Maximum13
Zeros11199
Zeros (%)95.3%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2024-02-26T23:19:04.716469image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4497570715
Coefficient of variation (CV)5.914758266
Kurtosis210.6136506
Mean0.07603980607
Median Absolute Deviation (MAD)0
Skewness11.5625037
Sum894
Variance0.2022814234
MonotonicityNot monotonic
2024-02-26T23:19:04.773827image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 11199
95.3%
1 387
 
3.3%
2 100
 
0.9%
3 37
 
0.3%
4 14
 
0.1%
5 7
 
0.1%
6 5
 
< 0.1%
8 3
 
< 0.1%
12 2
 
< 0.1%
7 2
 
< 0.1%
ValueCountFrequency (%)
0 11199
95.3%
1 387
 
3.3%
2 100
 
0.9%
3 37
 
0.3%
4 14
 
0.1%
ValueCountFrequency (%)
13 1
 
< 0.1%
12 2
 
< 0.1%
8 3
< 0.1%
7 2
 
< 0.1%
6 5
< 0.1%

juv_other_count
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09356128264
Minimum0
Maximum17
Zeros11030
Zeros (%)93.8%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2024-02-26T23:19:04.826142image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum17
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4720030533
Coefficient of variation (CV)5.044854453
Kurtosis228.0548775
Mean0.09356128264
Median Absolute Deviation (MAD)0
Skewness10.94756104
Sum1100
Variance0.2227868824
MonotonicityNot monotonic
2024-02-26T23:19:04.882759image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 11030
93.8%
1 520
 
4.4%
2 127
 
1.1%
3 45
 
0.4%
4 19
 
0.2%
5 7
 
0.1%
7 3
 
< 0.1%
6 2
 
< 0.1%
17 1
 
< 0.1%
9 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0 11030
93.8%
1 520
 
4.4%
2 127
 
1.1%
3 45
 
0.4%
4 19
 
0.2%
ValueCountFrequency (%)
17 1
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
7 3
< 0.1%
Distinct705
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
2024-02-26T23:19:05.098114image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters305682
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row2013-08-14 00:00:00.000000
2nd row2014-12-31 00:00:00.000000
3rd row2013-01-27 00:00:00.000000
4th row2013-04-14 00:00:00.000000
5th row2013-01-13 00:00:00.000000
ValueCountFrequency (%)
00:00:00.000000 11754
50.0%
2013-03-20 39
 
0.2%
2013-04-20 38
 
0.2%
2013-09-23 35
 
0.1%
2013-02-20 34
 
0.1%
2013-02-22 33
 
0.1%
2013-09-26 33
 
0.1%
2014-11-12 32
 
0.1%
2013-08-27 32
 
0.1%
2013-02-07 32
 
0.1%
Other values (696) 11452
48.7%
2024-02-26T23:19:05.382569image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 167264
54.7%
- 23514
 
7.7%
: 23514
 
7.7%
1 22161
 
7.2%
2 18930
 
6.2%
11757
 
3.8%
. 11757
 
3.8%
3 9130
 
3.0%
4 7853
 
2.6%
5 2144
 
0.7%
Other values (4) 7658
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 235140
76.9%
Other Punctuation 35271
 
11.5%
Dash Punctuation 23514
 
7.7%
Space Separator 11757
 
3.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 167264
71.1%
1 22161
 
9.4%
2 18930
 
8.1%
3 9130
 
3.9%
4 7853
 
3.3%
5 2144
 
0.9%
9 2040
 
0.9%
8 1996
 
0.8%
7 1838
 
0.8%
6 1784
 
0.8%
Other Punctuation
ValueCountFrequency (%)
: 23514
66.7%
. 11757
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 23514
100.0%
Space Separator
ValueCountFrequency (%)
11757
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 305682
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 167264
54.7%
- 23514
 
7.7%
: 23514
 
7.7%
1 22161
 
7.2%
2 18930
 
6.2%
11757
 
3.8%
. 11757
 
3.8%
3 9130
 
3.0%
4 7853
 
2.6%
5 2144
 
0.7%
Other values (4) 7658
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 305682
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 167264
54.7%
- 23514
 
7.7%
: 23514
 
7.7%
1 22161
 
7.2%
2 18930
 
6.2%
11757
 
3.8%
. 11757
 
3.8%
3 9130
 
3.0%
4 7853
 
2.6%
5 2144
 
0.7%
Other values (4) 7658
 
2.5%

decile_score
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.371268181
Minimum-1
Maximum10
Zeros0
Zeros (%)0.0%
Negative15
Negative (%)0.1%
Memory size92.0 KiB
2024-02-26T23:19:05.452407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q12
median4
Q37
95-th percentile10
Maximum10
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.877598451
Coefficient of variation (CV)0.658298309
Kurtosis-1.054388155
Mean4.371268181
Median Absolute Deviation (MAD)2
Skewness0.4352973678
Sum51393
Variance8.280572847
MonotonicityNot monotonic
2024-02-26T23:19:05.505528image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 2577
21.9%
2 1572
13.4%
3 1259
10.7%
4 1199
10.2%
5 1034
8.8%
6 993
 
8.4%
7 900
 
7.7%
9 802
 
6.8%
8 796
 
6.8%
10 610
 
5.2%
ValueCountFrequency (%)
-1 15
 
0.1%
1 2577
21.9%
2 1572
13.4%
3 1259
10.7%
4 1199
10.2%
ValueCountFrequency (%)
10 610
5.2%
9 802
6.8%
8 796
6.8%
7 900
7.7%
6 993
8.4%

score_text
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing11757
Missing (%)100.0%
Memory size92.0 KiB

violent_recid
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing11757
Missing (%)100.0%
Memory size92.0 KiB

priors_count
Real number (ℝ)

ZEROS 

Distinct39
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.082163817
Minimum0
Maximum43
Zeros4184
Zeros (%)35.6%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2024-02-26T23:19:05.563945image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile13
Maximum43
Range43
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.687410017
Coefficient of variation (CV)1.520817937
Kurtosis8.424031856
Mean3.082163817
Median Absolute Deviation (MAD)1
Skewness2.582883548
Sum36237
Variance21.97181267
MonotonicityNot monotonic
2024-02-26T23:19:05.626833image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 4184
35.6%
1 2199
18.7%
2 1281
 
10.9%
3 857
 
7.3%
4 588
 
5.0%
5 474
 
4.0%
6 349
 
3.0%
7 315
 
2.7%
8 260
 
2.2%
9 201
 
1.7%
Other values (29) 1049
 
8.9%
ValueCountFrequency (%)
0 4184
35.6%
1 2199
18.7%
2 1281
 
10.9%
3 857
 
7.3%
4 588
 
5.0%
ValueCountFrequency (%)
43 1
< 0.1%
39 1
< 0.1%
38 2
< 0.1%
37 1
< 0.1%
36 1
< 0.1%

days_b_screening_arrest
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing742
Missing (%)6.3%
Memory size92.0 KiB

c_jail_in
Text

MISSING 

Distinct10578
Distinct (%)96.0%
Missing742
Missing (%)6.3%
Memory size92.0 KiB
2024-02-26T23:19:05.823648image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length19
Median length19
Mean length18.24448479
Min length0

Characters and Unicode

Total characters200963
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10577 ?
Unique (%)96.0%

Sample

1st row2013-08-13 06:03:42
2nd row2013-01-26 03:45:27
3rd row2013-04-13 04:58:34
4th row
5th row
ValueCountFrequency (%)
2013-03-20 35
 
0.2%
2013-02-22 34
 
0.2%
2014-11-14 34
 
0.2%
2013-02-13 33
 
0.2%
2013-03-22 31
 
0.1%
2013-04-24 31
 
0.1%
2013-04-07 30
 
0.1%
2014-01-04 30
 
0.1%
2013-02-07 30
 
0.1%
2013-09-23 29
 
0.1%
Other values (10131) 20837
98.5%
2024-02-26T23:19:06.096171image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 37765
18.8%
1 30436
15.1%
2 24486
12.2%
- 21154
10.5%
: 21154
10.5%
3 14733
 
7.3%
4 13410
 
6.7%
10577
 
5.3%
5 8541
 
4.3%
9 4860
 
2.4%
Other values (3) 13847
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 148078
73.7%
Dash Punctuation 21154
 
10.5%
Other Punctuation 21154
 
10.5%
Space Separator 10577
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 37765
25.5%
1 30436
20.6%
2 24486
16.5%
3 14733
 
9.9%
4 13410
 
9.1%
5 8541
 
5.8%
9 4860
 
3.3%
8 4739
 
3.2%
7 4645
 
3.1%
6 4463
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 21154
100.0%
Other Punctuation
ValueCountFrequency (%)
: 21154
100.0%
Space Separator
ValueCountFrequency (%)
10577
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 200963
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 37765
18.8%
1 30436
15.1%
2 24486
12.2%
- 21154
10.5%
: 21154
10.5%
3 14733
 
7.3%
4 13410
 
6.7%
10577
 
5.3%
5 8541
 
4.3%
9 4860
 
2.4%
Other values (3) 13847
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 200963
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 37765
18.8%
1 30436
15.1%
2 24486
12.2%
- 21154
10.5%
: 21154
10.5%
3 14733
 
7.3%
4 13410
 
6.7%
10577
 
5.3%
5 8541
 
4.3%
9 4860
 
2.4%
Other values (3) 13847
 
6.9%

c_jail_out
Text

MISSING 

Distinct10518
Distinct (%)95.5%
Missing742
Missing (%)6.3%
Memory size92.0 KiB
2024-02-26T23:19:06.309223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length19
Median length19
Mean length18.24448479
Min length0

Characters and Unicode

Total characters200963
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10468 ?
Unique (%)95.0%

Sample

1st row2013-08-14 05:41:20
2nd row2013-02-05 05:36:53
3rd row2013-04-14 07:02:04
4th row
5th row
ValueCountFrequency (%)
2013-11-26 36
 
0.2%
2013-02-15 33
 
0.2%
2013-04-30 33
 
0.2%
2013-04-26 31
 
0.1%
2013-05-14 30
 
0.1%
2014-02-04 29
 
0.1%
2013-02-20 28
 
0.1%
2013-04-18 27
 
0.1%
2013-09-14 27
 
0.1%
2013-09-11 26
 
0.1%
Other values (9896) 20854
98.6%
2024-02-26T23:19:06.582963image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 39440
19.6%
1 28872
14.4%
2 23643
11.8%
- 21154
10.5%
: 21154
10.5%
3 14083
 
7.0%
4 13057
 
6.5%
10577
 
5.3%
5 8493
 
4.2%
8 5798
 
2.9%
Other values (3) 14692
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 148078
73.7%
Dash Punctuation 21154
 
10.5%
Other Punctuation 21154
 
10.5%
Space Separator 10577
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39440
26.6%
1 28872
19.5%
2 23643
16.0%
3 14083
 
9.5%
4 13057
 
8.8%
5 8493
 
5.7%
8 5798
 
3.9%
9 5264
 
3.6%
7 4864
 
3.3%
6 4564
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 21154
100.0%
Other Punctuation
ValueCountFrequency (%)
: 21154
100.0%
Space Separator
ValueCountFrequency (%)
10577
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 200963
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39440
19.6%
1 28872
14.4%
2 23643
11.8%
- 21154
10.5%
: 21154
10.5%
3 14083
 
7.0%
4 13057
 
6.5%
10577
 
5.3%
5 8493
 
4.2%
8 5798
 
2.9%
Other values (3) 14692
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 200963
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39440
19.6%
1 28872
14.4%
2 23643
11.8%
- 21154
10.5%
: 21154
10.5%
3 14083
 
7.0%
4 13057
 
6.5%
10577
 
5.3%
5 8493
 
4.2%
8 5798
 
2.9%
Other values (3) 14692
 
7.3%

c_case_number
Text

MISSING 

Distinct11015
Distinct (%)100.0%
Missing742
Missing (%)6.3%
Memory size92.0 KiB
2024-02-26T23:19:06.739701image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters143195
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11015 ?
Unique (%)100.0%

Sample

1st row13011352CF10A
2nd row13001275CF10A
3rd row13005330CF10A
4th row13000570CF10A
5th row12014130CF10A
ValueCountFrequency (%)
13017918cf10a 1
 
< 0.1%
14004524mm10a 1
 
< 0.1%
14004186cf10a 1
 
< 0.1%
13001275cf10a 1
 
< 0.1%
13005330cf10a 1
 
< 0.1%
13000570cf10a 1
 
< 0.1%
12014130cf10a 1
 
< 0.1%
13022355mm10a 1
 
< 0.1%
14002304cf10a 1
 
< 0.1%
13012216cf10a 1
 
< 0.1%
Other values (11005) 11005
99.9%
2024-02-26T23:19:06.947621image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 32649
22.8%
1 30953
21.6%
A 11001
 
7.7%
3 10118
 
7.1%
4 9270
 
6.5%
C 7658
 
5.3%
F 7416
 
5.2%
M 6345
 
4.4%
2 5675
 
4.0%
5 4654
 
3.3%
Other values (11) 17456
12.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 110150
76.9%
Uppercase Letter 33045
 
23.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 11001
33.3%
C 7658
23.2%
F 7416
22.4%
M 6345
19.2%
U 274
 
0.8%
T 241
 
0.7%
O 84
 
0.3%
B 11
 
< 0.1%
I 8
 
< 0.1%
N 6
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 32649
29.6%
1 30953
28.1%
3 10118
 
9.2%
4 9270
 
8.4%
2 5675
 
5.2%
5 4654
 
4.2%
6 4607
 
4.2%
7 4335
 
3.9%
8 4017
 
3.6%
9 3872
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 110150
76.9%
Latin 33045
 
23.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 11001
33.3%
C 7658
23.2%
F 7416
22.4%
M 6345
19.2%
U 274
 
0.8%
T 241
 
0.7%
O 84
 
0.3%
B 11
 
< 0.1%
I 8
 
< 0.1%
N 6
 
< 0.1%
Common
ValueCountFrequency (%)
0 32649
29.6%
1 30953
28.1%
3 10118
 
9.2%
4 9270
 
8.4%
2 5675
 
5.2%
5 4654
 
4.2%
6 4607
 
4.2%
7 4335
 
3.9%
8 4017
 
3.6%
9 3872
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 143195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32649
22.8%
1 30953
21.6%
A 11001
 
7.7%
3 10118
 
7.1%
4 9270
 
6.5%
C 7658
 
5.3%
F 7416
 
5.2%
M 6345
 
4.4%
2 5675
 
4.0%
5 4654
 
3.3%
Other values (11) 17456
12.2%

c_days_from_compas
Real number (ℝ)

MISSING  ZEROS 

Distinct680
Distinct (%)6.2%
Missing742
Missing (%)6.3%
Infinite0
Infinite (%)0.0%
Mean63.5876532
Minimum0
Maximum9485
Zeros1332
Zeros (%)11.3%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2024-02-26T23:19:07.023784image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile248
Maximum9485
Range9485
Interquartile range (IQR)1

Descriptive statistics

Standard deviation341.8997109
Coefficient of variation (CV)5.376825432
Kurtosis195.3463029
Mean63.5876532
Median Absolute Deviation (MAD)0
Skewness11.76984345
Sum700418
Variance116895.4123
MonotonicityNot monotonic
2024-02-26T23:19:07.089722image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6775
57.6%
0 1332
 
11.3%
2 344
 
2.9%
3 137
 
1.2%
4 105
 
0.9%
5 65
 
0.6%
23 44
 
0.4%
7 43
 
0.4%
6 42
 
0.4%
22 38
 
0.3%
Other values (670) 2090
 
17.8%
(Missing) 742
 
6.3%
ValueCountFrequency (%)
0 1332
 
11.3%
1 6775
57.6%
2 344
 
2.9%
3 137
 
1.2%
4 105
 
0.9%
ValueCountFrequency (%)
9485 1
< 0.1%
8023 1
< 0.1%
7604 1
< 0.1%
6594 1
< 0.1%
6323 1
< 0.1%

c_arrest_date
Text

MISSING 

Distinct802
Distinct (%)43.2%
Missing9899
Missing (%)84.2%
Memory size92.0 KiB
2024-02-26T23:19:07.322185image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters48308
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique353 ?
Unique (%)19.0%

Sample

1st row2013-01-09 00:00:00.000000
2nd row2013-08-29 00:00:00.000000
3rd row2012-06-27 00:00:00.000000
4th row2014-05-01 00:00:00.000000
5th row2002-11-16 00:00:00.000000
ValueCountFrequency (%)
00:00:00.000000 1858
50.0%
2013-02-06 9
 
0.2%
2013-01-10 9
 
0.2%
2013-01-15 9
 
0.2%
2013-05-15 9
 
0.2%
2013-04-17 8
 
0.2%
2013-03-22 8
 
0.2%
2013-02-19 8
 
0.2%
2013-10-22 8
 
0.2%
2013-04-16 8
 
0.2%
Other values (793) 1782
48.0%
2024-02-26T23:19:07.626826image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 26481
54.8%
- 3716
 
7.7%
: 3716
 
7.7%
1 3473
 
7.2%
2 3110
 
6.4%
1858
 
3.8%
. 1858
 
3.8%
3 1425
 
2.9%
4 1028
 
2.1%
5 373
 
0.8%
Other values (4) 1270
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37160
76.9%
Other Punctuation 5574
 
11.5%
Dash Punctuation 3716
 
7.7%
Space Separator 1858
 
3.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26481
71.3%
1 3473
 
9.3%
2 3110
 
8.4%
3 1425
 
3.8%
4 1028
 
2.8%
5 373
 
1.0%
9 354
 
1.0%
8 331
 
0.9%
6 310
 
0.8%
7 275
 
0.7%
Other Punctuation
ValueCountFrequency (%)
: 3716
66.7%
. 1858
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 3716
100.0%
Space Separator
ValueCountFrequency (%)
1858
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48308
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 26481
54.8%
- 3716
 
7.7%
: 3716
 
7.7%
1 3473
 
7.2%
2 3110
 
6.4%
1858
 
3.8%
. 1858
 
3.8%
3 1425
 
2.9%
4 1028
 
2.1%
5 373
 
0.8%
Other values (4) 1270
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48308
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26481
54.8%
- 3716
 
7.7%
: 3716
 
7.7%
1 3473
 
7.2%
2 3110
 
6.4%
1858
 
3.8%
. 1858
 
3.8%
3 1425
 
2.9%
4 1028
 
2.1%
5 373
 
0.8%
Other values (4) 1270
 
2.6%

c_offense_date
Text

MISSING 

Distinct1036
Distinct (%)11.3%
Missing2600
Missing (%)22.1%
Memory size92.0 KiB
2024-02-26T23:19:07.836995image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters238082
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique271 ?
Unique (%)3.0%

Sample

1st row2013-08-13 00:00:00.000000
2nd row2013-01-26 00:00:00.000000
3rd row2013-04-13 00:00:00.000000
4th row2013-01-12 00:00:00.000000
5th row2013-11-30 00:00:00.000000
ValueCountFrequency (%)
00:00:00.000000 9157
50.0%
2013-03-20 29
 
0.2%
2013-01-14 28
 
0.2%
2013-02-22 27
 
0.1%
2013-04-19 26
 
0.1%
2013-01-09 26
 
0.1%
2013-03-19 26
 
0.1%
2013-01-11 26
 
0.1%
2014-11-14 26
 
0.1%
2014-11-13 26
 
0.1%
Other values (1027) 8917
48.7%
2024-02-26T23:19:08.100148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 130319
54.7%
- 18314
 
7.7%
: 18314
 
7.7%
1 17432
 
7.3%
2 14811
 
6.2%
9157
 
3.8%
. 9157
 
3.8%
3 6911
 
2.9%
4 5931
 
2.5%
5 1722
 
0.7%
Other values (4) 6014
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 183140
76.9%
Other Punctuation 27471
 
11.5%
Dash Punctuation 18314
 
7.7%
Space Separator 9157
 
3.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 130319
71.2%
1 17432
 
9.5%
2 14811
 
8.1%
3 6911
 
3.8%
4 5931
 
3.2%
5 1722
 
0.9%
9 1646
 
0.9%
8 1634
 
0.9%
6 1402
 
0.8%
7 1332
 
0.7%
Other Punctuation
ValueCountFrequency (%)
: 18314
66.7%
. 9157
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 18314
100.0%
Space Separator
ValueCountFrequency (%)
9157
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 238082
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 130319
54.7%
- 18314
 
7.7%
: 18314
 
7.7%
1 17432
 
7.3%
2 14811
 
6.2%
9157
 
3.8%
. 9157
 
3.8%
3 6911
 
2.9%
4 5931
 
2.5%
5 1722
 
0.7%
Other values (4) 6014
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 238082
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 130319
54.7%
- 18314
 
7.7%
: 18314
 
7.7%
1 17432
 
7.3%
2 14811
 
6.2%
9157
 
3.8%
. 9157
 
3.8%
3 6911
 
2.9%
4 5931
 
2.5%
5 1722
 
0.7%
Other values (4) 6014
 
2.5%

c_charge_degree
Text

MISSING 

Distinct14
Distinct (%)0.1%
Missing742
Missing (%)6.3%
Memory size92.0 KiB
2024-02-26T23:19:08.166685image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.008352247
Min length3

Characters and Unicode

Total characters44152
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row(F3)
2nd row(F3)
3rd row(F3)
4th row(F3)
5th row(F7)
ValueCountFrequency (%)
f3 5913
53.7%
m1 2831
25.7%
f2 953
 
8.7%
m2 857
 
7.8%
f1 221
 
2.0%
f7 128
 
1.2%
mo3 83
 
0.8%
f6 10
 
0.1%
ni0 8
 
0.1%
f5 7
 
0.1%
Other values (4) 4
 
< 0.1%
2024-02-26T23:19:08.376992image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 11015
24.9%
) 11015
24.9%
F 7232
16.4%
3 5997
13.6%
M 3771
 
8.5%
1 3052
 
6.9%
2 1810
 
4.1%
7 128
 
0.3%
O 84
 
0.2%
6 10
 
< 0.1%
Other values (7) 38
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11110
25.2%
Open Punctuation 11015
24.9%
Close Punctuation 11015
24.9%
Decimal Number 11012
24.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 7232
65.1%
M 3771
33.9%
O 84
 
0.8%
N 8
 
0.1%
I 8
 
0.1%
C 3
 
< 0.1%
X 2
 
< 0.1%
T 2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
3 5997
54.5%
1 3052
27.7%
2 1810
 
16.4%
7 128
 
1.2%
6 10
 
0.1%
0 8
 
0.1%
5 7
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 11015
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11015
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33042
74.8%
Latin 11110
 
25.2%

Most frequent character per script

Common
ValueCountFrequency (%)
( 11015
33.3%
) 11015
33.3%
3 5997
18.1%
1 3052
 
9.2%
2 1810
 
5.5%
7 128
 
0.4%
6 10
 
< 0.1%
0 8
 
< 0.1%
5 7
 
< 0.1%
Latin
ValueCountFrequency (%)
F 7232
65.1%
M 3771
33.9%
O 84
 
0.8%
N 8
 
0.1%
I 8
 
0.1%
C 3
 
< 0.1%
X 2
 
< 0.1%
T 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 11015
24.9%
) 11015
24.9%
F 7232
16.4%
3 5997
13.6%
M 3771
 
8.5%
1 3052
 
6.9%
2 1810
 
4.1%
7 128
 
0.3%
O 84
 
0.2%
6 10
 
< 0.1%
Other values (7) 38
 
0.1%

c_charge_desc
Text

MISSING 

Distinct531
Distinct (%)4.8%
Missing749
Missing (%)6.4%
Memory size92.0 KiB
2024-02-26T23:19:08.553429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length52
Median length43
Mean length22.12836119
Min length5

Characters and Unicode

Total characters243589
Distinct characters72
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique232 ?
Unique (%)2.1%

Sample

1st rowAggravated Assault w/Firearm
2nd rowFelony Battery w/Prior Convict
3rd rowPossession of Cocaine
4th rowPossession of Cannabis
5th rowarrest case no charge
ValueCountFrequency (%)
battery 2492
 
6.7%
no 1956
 
5.2%
charge 1860
 
5.0%
arrest 1859
 
5.0%
case 1858
 
5.0%
of 1310
 
3.5%
possession 1187
 
3.2%
theft 1052
 
2.8%
the 944
 
2.5%
grand 864
 
2.3%
Other values (792) 21976
58.8%
2024-02-26T23:19:08.805982image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 27461
 
11.3%
26380
 
10.8%
r 18394
 
7.6%
a 16656
 
6.8%
n 14839
 
6.1%
s 14454
 
5.9%
t 14349
 
5.9%
o 12765
 
5.2%
i 11077
 
4.5%
c 8785
 
3.6%
Other values (62) 78429
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 183003
75.1%
Uppercase Letter 28108
 
11.5%
Space Separator 26380
 
10.8%
Other Punctuation 2554
 
1.0%
Decimal Number 2411
 
1.0%
Open Punctuation 382
 
0.2%
Close Punctuation 382
 
0.2%
Currency Symbol 141
 
0.1%
Math Symbol 121
 
< 0.1%
Dash Punctuation 107
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 27461
15.0%
r 18394
10.1%
a 16656
9.1%
n 14839
 
8.1%
s 14454
 
7.9%
t 14349
 
7.8%
o 12765
 
7.0%
i 11077
 
6.1%
c 8785
 
4.8%
g 6596
 
3.6%
Other values (16) 37627
20.6%
Uppercase Letter
ValueCountFrequency (%)
D 3431
12.2%
B 3259
11.6%
P 2896
 
10.3%
C 2241
 
8.0%
T 1662
 
5.9%
W 1535
 
5.5%
I 1490
 
5.3%
A 1475
 
5.2%
O 1316
 
4.7%
L 1276
 
4.5%
Other values (14) 7527
26.8%
Decimal Number
ValueCountFrequency (%)
3 859
35.6%
0 633
26.3%
1 332
 
13.8%
2 223
 
9.2%
5 158
 
6.6%
4 133
 
5.5%
6 60
 
2.5%
9 6
 
0.2%
8 6
 
0.2%
7 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 2322
90.9%
, 119
 
4.7%
. 110
 
4.3%
# 3
 
0.1%
Math Symbol
ValueCountFrequency (%)
< 53
43.8%
+ 39
32.2%
> 29
24.0%
Space Separator
ValueCountFrequency (%)
26380
100.0%
Open Punctuation
ValueCountFrequency (%)
( 382
100.0%
Close Punctuation
ValueCountFrequency (%)
) 382
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 141
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 107
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 211111
86.7%
Common 32478
 
13.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 27461
13.0%
r 18394
 
8.7%
a 16656
 
7.9%
n 14839
 
7.0%
s 14454
 
6.8%
t 14349
 
6.8%
o 12765
 
6.0%
i 11077
 
5.2%
c 8785
 
4.2%
g 6596
 
3.1%
Other values (40) 65735
31.1%
Common
ValueCountFrequency (%)
26380
81.2%
/ 2322
 
7.1%
3 859
 
2.6%
0 633
 
1.9%
( 382
 
1.2%
) 382
 
1.2%
1 332
 
1.0%
2 223
 
0.7%
5 158
 
0.5%
$ 141
 
0.4%
Other values (12) 666
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 243589
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 27461
 
11.3%
26380
 
10.8%
r 18394
 
7.6%
a 16656
 
6.8%
n 14839
 
6.1%
s 14454
 
5.9%
t 14349
 
5.9%
o 12765
 
5.2%
i 11077
 
4.5%
c 8785
 
3.6%
Other values (62) 78429
32.2%

is_recid
Real number (ℝ)

ZEROS 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2538062431
Minimum-1
Maximum1
Zeros7335
Zeros (%)62.4%
Negative719
Negative (%)6.1%
Memory size92.0 KiB
2024-02-26T23:19:08.871386image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q10
median0
Q31
95-th percentile1
Maximum1
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5583236169
Coefficient of variation (CV)2.199802535
Kurtosis-0.4124048819
Mean0.2538062431
Median Absolute Deviation (MAD)0
Skewness0.0007091284407
Sum2984
Variance0.3117252612
MonotonicityNot monotonic
2024-02-26T23:19:08.925500image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
0 7335
62.4%
1 3703
31.5%
-1 719
 
6.1%
ValueCountFrequency (%)
-1 719
 
6.1%
0 7335
62.4%
1 3703
31.5%
ValueCountFrequency (%)
1 3703
31.5%
0 7335
62.4%
-1 719
 
6.1%

num_r_cases
Real number (ℝ)

MISSING 

Distinct15
Distinct (%)0.4%
Missing8054
Missing (%)68.5%
Infinite0
Infinite (%)0.0%
Mean1.731028895
Minimum1
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2024-02-26T23:19:08.979702image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum55
Range54
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.588221841
Coefficient of variation (CV)0.9175016346
Kurtosis372.9436449
Mean1.731028895
Median Absolute Deviation (MAD)0
Skewness13.2689383
Sum6410
Variance2.522448617
MonotonicityNot monotonic
2024-02-26T23:19:09.035127image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 2236
 
19.0%
2 874
 
7.4%
3 311
 
2.6%
4 152
 
1.3%
5 60
 
0.5%
6 40
 
0.3%
7 10
 
0.1%
8 5
 
< 0.1%
9 5
 
< 0.1%
12 4
 
< 0.1%
Other values (5) 6
 
0.1%
(Missing) 8054
68.5%
ValueCountFrequency (%)
1 2236
19.0%
2 874
 
7.4%
3 311
 
2.6%
4 152
 
1.3%
5 60
 
0.5%
ValueCountFrequency (%)
55 1
 
< 0.1%
29 1
 
< 0.1%
18 1
 
< 0.1%
15 2
< 0.1%
12 4
< 0.1%

r_case_number
Text

MISSING 

Distinct3703
Distinct (%)100.0%
Missing8054
Missing (%)68.5%
Memory size92.0 KiB
2024-02-26T23:19:09.183192image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters48139
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3703 ?
Unique (%)100.0%

Sample

1st row13009779CF10A
2nd row13011511MM10A
3rd row14004485CF10A
4th row15002891MM10A
5th row14010414TC40A
ValueCountFrequency (%)
14008267mm10a 1
 
< 0.1%
15002656cf10a 1
 
< 0.1%
13011511mm10a 1
 
< 0.1%
14004485cf10a 1
 
< 0.1%
15002891mm10a 1
 
< 0.1%
14010414tc40a 1
 
< 0.1%
14007704cf10a 1
 
< 0.1%
14009921mm10a 1
 
< 0.1%
14010409cf10a 1
 
< 0.1%
15005595cf10a 1
 
< 0.1%
Other values (3693) 3693
99.7%
2024-02-26T23:19:09.401431image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11005
22.9%
1 9363
19.4%
A 3702
 
7.7%
4 3364
 
7.0%
M 2737
 
5.7%
5 2720
 
5.7%
3 2653
 
5.5%
C 2287
 
4.8%
2 2056
 
4.3%
6 1760
 
3.7%
Other values (10) 6492
13.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37030
76.9%
Uppercase Letter 11106
 
23.1%
Lowercase Letter 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11005
29.7%
1 9363
25.3%
4 3364
 
9.1%
5 2720
 
7.3%
3 2653
 
7.2%
2 2056
 
5.6%
6 1760
 
4.8%
7 1441
 
3.9%
8 1337
 
3.6%
9 1331
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
A 3702
33.3%
M 2737
24.6%
C 2287
20.6%
F 1504
13.5%
T 783
 
7.1%
U 48
 
0.4%
O 45
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
t 1
33.3%
c 1
33.3%
a 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 37030
76.9%
Latin 11109
 
23.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11005
29.7%
1 9363
25.3%
4 3364
 
9.1%
5 2720
 
7.3%
3 2653
 
7.2%
2 2056
 
5.6%
6 1760
 
4.8%
7 1441
 
3.9%
8 1337
 
3.6%
9 1331
 
3.6%
Latin
ValueCountFrequency (%)
A 3702
33.3%
M 2737
24.6%
C 2287
20.6%
F 1504
13.5%
T 783
 
7.0%
U 48
 
0.4%
O 45
 
0.4%
t 1
 
< 0.1%
c 1
 
< 0.1%
a 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48139
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11005
22.9%
1 9363
19.4%
A 3702
 
7.7%
4 3364
 
7.0%
M 2737
 
5.7%
5 2720
 
5.7%
3 2653
 
5.5%
C 2287
 
4.8%
2 2056
 
4.3%
6 1760
 
3.7%
Other values (10) 6492
13.5%

r_charge_degree
Text

MISSING 

Distinct10
Distinct (%)0.3%
Missing8054
Missing (%)68.5%
Memory size92.0 KiB
2024-02-26T23:19:09.468555image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.011342155
Min length4

Characters and Unicode

Total characters14854
Distinct characters12
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row(F3)
2nd row(M1)
3rd row(F2)
4th row(M1)
5th row(M2)
ValueCountFrequency (%)
m1 1277
34.5%
m2 1182
31.9%
f3 952
25.7%
f2 182
 
4.9%
f1 57
 
1.5%
mo3 40
 
1.1%
f7 7
 
0.2%
f6 3
 
0.1%
co3 2
 
0.1%
f5 1
 
< 0.1%
2024-02-26T23:19:09.586668image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 3703
24.9%
) 3703
24.9%
M 2499
16.8%
2 1364
 
9.2%
1 1334
 
9.0%
F 1202
 
8.1%
3 994
 
6.7%
O 42
 
0.3%
7 7
 
< 0.1%
6 3
 
< 0.1%
Other values (2) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3745
25.2%
Open Punctuation 3703
24.9%
Close Punctuation 3703
24.9%
Decimal Number 3703
24.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1364
36.8%
1 1334
36.0%
3 994
26.8%
7 7
 
0.2%
6 3
 
0.1%
5 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
M 2499
66.7%
F 1202
32.1%
O 42
 
1.1%
C 2
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 3703
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3703
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11109
74.8%
Latin 3745
 
25.2%

Most frequent character per script

Common
ValueCountFrequency (%)
( 3703
33.3%
) 3703
33.3%
2 1364
 
12.3%
1 1334
 
12.0%
3 994
 
8.9%
7 7
 
0.1%
6 3
 
< 0.1%
5 1
 
< 0.1%
Latin
ValueCountFrequency (%)
M 2499
66.7%
F 1202
32.1%
O 42
 
1.1%
C 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14854
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 3703
24.9%
) 3703
24.9%
M 2499
16.8%
2 1364
 
9.2%
1 1334
 
9.0%
F 1202
 
8.1%
3 994
 
6.7%
O 42
 
0.3%
7 7
 
< 0.1%
6 3
 
< 0.1%
Other values (2) 3
 
< 0.1%

r_days_from_arrest
Real number (ℝ)

MISSING  ZEROS 

Distinct214
Distinct (%)8.7%
Missing9297
Missing (%)79.1%
Infinite0
Infinite (%)0.0%
Mean20.41056911
Minimum-1
Maximum993
Zeros1779
Zeros (%)15.1%
Negative6
Negative (%)0.1%
Memory size92.0 KiB
2024-02-26T23:19:09.658016image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q31
95-th percentile137.1
Maximum993
Range994
Interquartile range (IQR)1

Descriptive statistics

Standard deviation74.35484021
Coefficient of variation (CV)3.642957716
Kurtosis47.61467387
Mean20.41056911
Median Absolute Deviation (MAD)0
Skewness5.997136417
Sum50210
Variance5528.642263
MonotonicityNot monotonic
2024-02-26T23:19:09.724190image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1779
 
15.1%
1 306
 
2.6%
51 7
 
0.1%
77 6
 
0.1%
-1 6
 
0.1%
78 6
 
0.1%
4 5
 
< 0.1%
6 5
 
< 0.1%
3 5
 
< 0.1%
39 5
 
< 0.1%
Other values (204) 330
 
2.8%
(Missing) 9297
79.1%
ValueCountFrequency (%)
-1 6
 
0.1%
0 1779
15.1%
1 306
 
2.6%
2 2
 
< 0.1%
3 5
 
< 0.1%
ValueCountFrequency (%)
993 1
< 0.1%
862 1
< 0.1%
825 1
< 0.1%
786 1
< 0.1%
758 1
< 0.1%

r_offense_date
Text

MISSING 

Distinct1090
Distinct (%)29.4%
Missing8054
Missing (%)68.5%
Memory size92.0 KiB
2024-02-26T23:19:09.959559image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters96278
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique175 ?
Unique (%)4.7%

Sample

1st row2013-07-05 00:00:00.000000
2nd row2013-06-16 00:00:00.000000
3rd row2014-03-31 00:00:00.000000
4th row2015-01-06 00:00:00.000000
5th row2014-02-08 00:00:00.000000
ValueCountFrequency (%)
00:00:00.000000 3703
50.0%
2014-12-08 12
 
0.2%
2015-02-10 11
 
0.1%
2015-01-28 11
 
0.1%
2014-04-03 10
 
0.1%
2014-06-05 10
 
0.1%
2014-09-15 10
 
0.1%
2015-03-11 10
 
0.1%
2014-10-17 10
 
0.1%
2014-06-07 10
 
0.1%
Other values (1081) 3609
48.7%
2024-02-26T23:19:10.265271image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 52729
54.8%
- 7406
 
7.7%
: 7406
 
7.7%
1 6917
 
7.2%
2 5858
 
6.1%
3703
 
3.8%
. 3703
 
3.8%
4 2173
 
2.3%
5 1820
 
1.9%
3 1758
 
1.8%
Other values (4) 2805
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74060
76.9%
Other Punctuation 11109
 
11.5%
Dash Punctuation 7406
 
7.7%
Space Separator 3703
 
3.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 52729
71.2%
1 6917
 
9.3%
2 5858
 
7.9%
4 2173
 
2.9%
5 1820
 
2.5%
3 1758
 
2.4%
6 837
 
1.1%
8 684
 
0.9%
7 652
 
0.9%
9 632
 
0.9%
Other Punctuation
ValueCountFrequency (%)
: 7406
66.7%
. 3703
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 7406
100.0%
Space Separator
ValueCountFrequency (%)
3703
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 96278
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 52729
54.8%
- 7406
 
7.7%
: 7406
 
7.7%
1 6917
 
7.2%
2 5858
 
6.1%
3703
 
3.8%
. 3703
 
3.8%
4 2173
 
2.3%
5 1820
 
1.9%
3 1758
 
1.8%
Other values (4) 2805
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 96278
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 52729
54.8%
- 7406
 
7.7%
: 7406
 
7.7%
1 6917
 
7.2%
2 5858
 
6.1%
3703
 
3.8%
. 3703
 
3.8%
4 2173
 
2.3%
5 1820
 
1.9%
3 1758
 
1.8%
Other values (4) 2805
 
2.9%

r_charge_desc
Text

MISSING 

Distinct352
Distinct (%)9.7%
Missing8114
Missing (%)69.0%
Memory size92.0 KiB
2024-02-26T23:19:10.445070image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length52
Median length36
Mean length24.91243481
Min length6

Characters and Unicode

Total characters90756
Distinct characters72
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique171 ?
Unique (%)4.7%

Sample

1st rowFelony Battery (Dom Strang)
2nd rowDriving Under The Influence
3rd rowPoss of Firearm by Convic Felo
4th rowBattery
5th rowDriving License Suspended
ValueCountFrequency (%)
license 512
 
4.0%
theft 497
 
3.9%
w/o 415
 
3.2%
driving 373
 
2.9%
possess 336
 
2.6%
battery 318
 
2.5%
of 308
 
2.4%
petit 288
 
2.2%
or 287
 
2.2%
suspended 279
 
2.2%
Other values (596) 9234
71.9%
2024-02-26T23:19:10.686922image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 9570
 
10.5%
9215
 
10.2%
s 7189
 
7.9%
i 5888
 
6.5%
n 5570
 
6.1%
t 4986
 
5.5%
r 4911
 
5.4%
a 4241
 
4.7%
o 3567
 
3.9%
c 2622
 
2.9%
Other values (62) 32997
36.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 64332
70.9%
Uppercase Letter 13119
 
14.5%
Space Separator 9215
 
10.2%
Decimal Number 1838
 
2.0%
Other Punctuation 1734
 
1.9%
Currency Symbol 227
 
0.3%
Dash Punctuation 97
 
0.1%
Math Symbol 80
 
0.1%
Open Punctuation 57
 
0.1%
Close Punctuation 57
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9570
14.9%
s 7189
11.2%
i 5888
9.2%
n 5570
8.7%
t 4986
 
7.8%
r 4911
 
7.6%
a 4241
 
6.6%
o 3567
 
5.5%
c 2622
 
4.1%
l 2215
 
3.4%
Other values (16) 13573
21.1%
Uppercase Letter
ValueCountFrequency (%)
O 1597
12.2%
P 1408
10.7%
D 1302
9.9%
L 1228
9.4%
T 901
 
6.9%
S 887
 
6.8%
C 860
 
6.6%
V 770
 
5.9%
W 725
 
5.5%
R 494
 
3.8%
Other values (14) 2947
22.5%
Decimal Number
ValueCountFrequency (%)
0 789
42.9%
2 376
20.5%
1 311
 
16.9%
3 261
 
14.2%
5 31
 
1.7%
6 31
 
1.7%
4 18
 
1.0%
8 11
 
0.6%
9 8
 
0.4%
7 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
/ 1713
98.8%
, 15
 
0.9%
. 4
 
0.2%
" 2
 
0.1%
Math Symbol
ValueCountFrequency (%)
< 32
40.0%
> 29
36.2%
+ 19
23.8%
Space Separator
ValueCountFrequency (%)
9215
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 227
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 97
100.0%
Open Punctuation
ValueCountFrequency (%)
( 57
100.0%
Close Punctuation
ValueCountFrequency (%)
) 57
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 77451
85.3%
Common 13305
 
14.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9570
 
12.4%
s 7189
 
9.3%
i 5888
 
7.6%
n 5570
 
7.2%
t 4986
 
6.4%
r 4911
 
6.3%
a 4241
 
5.5%
o 3567
 
4.6%
c 2622
 
3.4%
l 2215
 
2.9%
Other values (40) 26692
34.5%
Common
ValueCountFrequency (%)
9215
69.3%
/ 1713
 
12.9%
0 789
 
5.9%
2 376
 
2.8%
1 311
 
2.3%
3 261
 
2.0%
$ 227
 
1.7%
- 97
 
0.7%
( 57
 
0.4%
) 57
 
0.4%
Other values (12) 202
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90756
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 9570
 
10.5%
9215
 
10.2%
s 7189
 
7.9%
i 5888
 
6.5%
n 5570
 
6.1%
t 4986
 
5.5%
r 4911
 
5.4%
a 4241
 
4.7%
o 3567
 
3.9%
c 2622
 
2.9%
Other values (62) 32997
36.4%

r_jail_in
Text

MISSING 

Distinct2460
Distinct (%)100.0%
Missing9297
Missing (%)79.1%
Memory size92.0 KiB
2024-02-26T23:19:10.903332image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters63960
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2460 ?
Unique (%)100.0%

Sample

1st row2013-06-16 09:05:47.000000
2nd row2014-03-31 08:10:09.000000
3rd row2015-01-06 03:55:34.000000
4th row2014-06-03 07:13:52.000000
5th row2014-06-25 02:15:57.000000
ValueCountFrequency (%)
2014-05-27 9
 
0.2%
2014-07-10 9
 
0.2%
2014-07-28 8
 
0.2%
2015-03-03 8
 
0.2%
2014-12-08 8
 
0.2%
2014-06-05 8
 
0.2%
2013-11-22 8
 
0.2%
2014-04-29 8
 
0.2%
2015-01-20 8
 
0.2%
2015-02-10 8
 
0.2%
Other values (3348) 4838
98.3%
2024-02-26T23:19:11.178229image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 23624
36.9%
1 6883
 
10.8%
2 5713
 
8.9%
- 4920
 
7.7%
: 4920
 
7.7%
4 2927
 
4.6%
3 2765
 
4.3%
5 2707
 
4.2%
2460
 
3.8%
. 2460
 
3.8%
Other values (4) 4581
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49200
76.9%
Other Punctuation 7380
 
11.5%
Dash Punctuation 4920
 
7.7%
Space Separator 2460
 
3.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23624
48.0%
1 6883
 
14.0%
2 5713
 
11.6%
4 2927
 
5.9%
3 2765
 
5.6%
5 2707
 
5.5%
8 1182
 
2.4%
6 1168
 
2.4%
7 1123
 
2.3%
9 1108
 
2.3%
Other Punctuation
ValueCountFrequency (%)
: 4920
66.7%
. 2460
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 4920
100.0%
Space Separator
ValueCountFrequency (%)
2460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 63960
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23624
36.9%
1 6883
 
10.8%
2 5713
 
8.9%
- 4920
 
7.7%
: 4920
 
7.7%
4 2927
 
4.6%
3 2765
 
4.3%
5 2707
 
4.2%
2460
 
3.8%
. 2460
 
3.8%
Other values (4) 4581
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23624
36.9%
1 6883
 
10.8%
2 5713
 
8.9%
- 4920
 
7.7%
: 4920
 
7.7%
4 2927
 
4.6%
3 2765
 
4.3%
5 2707
 
4.2%
2460
 
3.8%
. 2460
 
3.8%
Other values (4) 4581
 
7.2%

r_jail_out
Text

MISSING 

Distinct2459
Distinct (%)> 99.9%
Missing9297
Missing (%)79.1%
Memory size92.0 KiB
2024-02-26T23:19:11.400868image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters63960
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2458 ?
Unique (%)99.9%

Sample

1st row2013-06-16 07:18:55.000000
2nd row2014-04-18 04:54:59.000000
3rd row2015-01-07 03:38:44.000000
4th row2014-11-19 07:25:50.000000
5th row2014-06-28 05:02:21.000000
ValueCountFrequency (%)
2014-02-18 10
 
0.2%
2015-05-15 10
 
0.2%
2014-12-09 9
 
0.2%
2014-07-11 9
 
0.2%
2013-11-13 8
 
0.2%
2014-08-27 8
 
0.2%
2015-04-07 8
 
0.2%
2014-09-23 8
 
0.2%
2014-05-18 7
 
0.1%
2015-11-10 7
 
0.1%
Other values (3327) 4836
98.3%
2024-02-26T23:19:11.676726image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 23943
37.4%
1 6564
 
10.3%
2 5492
 
8.6%
- 4920
 
7.7%
: 4920
 
7.7%
4 2954
 
4.6%
5 2825
 
4.4%
3 2562
 
4.0%
2460
 
3.8%
. 2460
 
3.8%
Other values (4) 4860
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49200
76.9%
Other Punctuation 7380
 
11.5%
Dash Punctuation 4920
 
7.7%
Space Separator 2460
 
3.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23943
48.7%
1 6564
 
13.3%
2 5492
 
11.2%
4 2954
 
6.0%
5 2825
 
5.7%
3 2562
 
5.2%
8 1332
 
2.7%
9 1262
 
2.6%
6 1218
 
2.5%
7 1048
 
2.1%
Other Punctuation
ValueCountFrequency (%)
: 4920
66.7%
. 2460
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 4920
100.0%
Space Separator
ValueCountFrequency (%)
2460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 63960
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23943
37.4%
1 6564
 
10.3%
2 5492
 
8.6%
- 4920
 
7.7%
: 4920
 
7.7%
4 2954
 
4.6%
5 2825
 
4.4%
3 2562
 
4.0%
2460
 
3.8%
. 2460
 
3.8%
Other values (4) 4860
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23943
37.4%
1 6564
 
10.3%
2 5492
 
8.6%
- 4920
 
7.7%
: 4920
 
7.7%
4 2954
 
4.6%
5 2825
 
4.4%
3 2562
 
4.0%
2460
 
3.8%
. 2460
 
3.8%
Other values (4) 4860
 
7.6%

is_violent_recid
Real number (ℝ)

ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07501913754
Minimum0
Maximum1
Zeros10875
Zeros (%)92.5%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2024-02-26T23:19:11.744025image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2634334245
Coefficient of variation (CV)3.511549628
Kurtosis8.415124058
Mean0.07501913754
Median Absolute Deviation (MAD)0
Skewness3.227025345
Sum882
Variance0.06939716916
MonotonicityNot monotonic
2024-02-26T23:19:11.795056image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 10875
92.5%
1 882
 
7.5%
ValueCountFrequency (%)
0 10875
92.5%
1 882
 
7.5%
ValueCountFrequency (%)
1 882
 
7.5%
0 10875
92.5%

num_vr_cases
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing11757
Missing (%)100.0%
Memory size92.0 KiB

vr_case_number
Text

MISSING 

Distinct882
Distinct (%)100.0%
Missing10875
Missing (%)92.5%
Memory size92.0 KiB
2024-02-26T23:19:11.935362image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters11466
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique882 ?
Unique (%)100.0%

Sample

1st row13009779CF10A
2nd row15000258CF10A
3rd row14010409CF10A
4th row15002479CF10A
5th row15010523CF10A
ValueCountFrequency (%)
16000253cf10a 1
 
0.1%
16003005cf10a 1
 
0.1%
14005925mm10a 1
 
0.1%
15000258cf10a 1
 
0.1%
14010409cf10a 1
 
0.1%
15002479cf10a 1
 
0.1%
15010523cf10a 1
 
0.1%
14004719cf10a 1
 
0.1%
14008544mo10a 1
 
0.1%
14001759mm10a 1
 
0.1%
Other values (872) 872
98.9%
2024-02-26T23:19:12.146778image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2679
23.4%
1 2520
22.0%
A 882
 
7.7%
5 696
 
6.1%
M 686
 
6.0%
4 635
 
5.5%
C 536
 
4.7%
F 536
 
4.7%
3 519
 
4.5%
2 393
 
3.4%
Other values (5) 1384
12.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8820
76.9%
Uppercase Letter 2646
 
23.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2679
30.4%
1 2520
28.6%
5 696
 
7.9%
4 635
 
7.2%
3 519
 
5.9%
2 393
 
4.5%
6 379
 
4.3%
7 370
 
4.2%
8 319
 
3.6%
9 310
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
A 882
33.3%
M 686
25.9%
C 536
20.3%
F 536
20.3%
O 6
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 8820
76.9%
Latin 2646
 
23.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2679
30.4%
1 2520
28.6%
5 696
 
7.9%
4 635
 
7.2%
3 519
 
5.9%
2 393
 
4.5%
6 379
 
4.3%
7 370
 
4.2%
8 319
 
3.6%
9 310
 
3.5%
Latin
ValueCountFrequency (%)
A 882
33.3%
M 686
25.9%
C 536
20.3%
F 536
20.3%
O 6
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11466
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2679
23.4%
1 2520
22.0%
A 882
 
7.7%
5 696
 
6.1%
M 686
 
6.0%
4 635
 
5.5%
C 536
 
4.7%
F 536
 
4.7%
3 519
 
4.5%
2 393
 
3.4%
Other values (5) 1384
12.1%

vr_charge_degree
Text

MISSING 

Distinct9
Distinct (%)1.0%
Missing10875
Missing (%)92.5%
Memory size92.0 KiB
2024-02-26T23:19:12.210157image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.006802721
Min length4

Characters and Unicode

Total characters3534
Distinct characters11
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row(F3)
2nd row(F2)
3rd row(F1)
4th row(F3)
5th row(M1)
ValueCountFrequency (%)
m1 372
42.2%
f3 243
27.6%
f2 174
19.7%
f1 43
 
4.9%
m2 20
 
2.3%
f7 19
 
2.2%
mo3 6
 
0.7%
f6 4
 
0.5%
f5 1
 
0.1%
2024-02-26T23:19:12.321765image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 882
25.0%
) 882
25.0%
F 484
13.7%
1 415
11.7%
M 398
11.3%
3 249
 
7.0%
2 194
 
5.5%
7 19
 
0.5%
O 6
 
0.2%
6 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 888
25.1%
Open Punctuation 882
25.0%
Close Punctuation 882
25.0%
Decimal Number 882
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 415
47.1%
3 249
28.2%
2 194
22.0%
7 19
 
2.2%
6 4
 
0.5%
5 1
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
F 484
54.5%
M 398
44.8%
O 6
 
0.7%
Open Punctuation
ValueCountFrequency (%)
( 882
100.0%
Close Punctuation
ValueCountFrequency (%)
) 882
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2646
74.9%
Latin 888
 
25.1%

Most frequent character per script

Common
ValueCountFrequency (%)
( 882
33.3%
) 882
33.3%
1 415
15.7%
3 249
 
9.4%
2 194
 
7.3%
7 19
 
0.7%
6 4
 
0.2%
5 1
 
< 0.1%
Latin
ValueCountFrequency (%)
F 484
54.5%
M 398
44.8%
O 6
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3534
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 882
25.0%
) 882
25.0%
F 484
13.7%
1 415
11.7%
M 398
11.3%
3 249
 
7.0%
2 194
 
5.5%
7 19
 
0.5%
O 6
 
0.2%
6 4
 
0.1%

vr_offense_date
Text

MISSING 

Distinct599
Distinct (%)67.9%
Missing10875
Missing (%)92.5%
Memory size92.0 KiB
2024-02-26T23:19:12.560710image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters22932
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique396 ?
Unique (%)44.9%

Sample

1st row2013-07-05 00:00:00.000000
2nd row2015-01-06 00:00:00.000000
3rd row2014-07-16 00:00:00.000000
4th row2015-02-23 00:00:00.000000
5th row2015-08-15 00:00:00.000000
ValueCountFrequency (%)
00:00:00.000000 882
50.0%
2015-08-15 6
 
0.3%
2015-09-04 5
 
0.3%
2014-09-28 4
 
0.2%
2015-01-06 4
 
0.2%
2015-10-14 4
 
0.2%
2013-11-14 4
 
0.2%
2015-10-01 4
 
0.2%
2014-12-26 4
 
0.2%
2015-09-07 4
 
0.2%
Other values (590) 843
47.8%
2024-02-26T23:19:12.864955image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12566
54.8%
- 1764
 
7.7%
: 1764
 
7.7%
1 1623
 
7.1%
2 1374
 
6.0%
882
 
3.8%
. 882
 
3.8%
5 525
 
2.3%
4 476
 
2.1%
3 346
 
1.5%
Other values (4) 730
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17640
76.9%
Other Punctuation 2646
 
11.5%
Dash Punctuation 1764
 
7.7%
Space Separator 882
 
3.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12566
71.2%
1 1623
 
9.2%
2 1374
 
7.8%
5 525
 
3.0%
4 476
 
2.7%
3 346
 
2.0%
6 232
 
1.3%
7 177
 
1.0%
8 168
 
1.0%
9 153
 
0.9%
Other Punctuation
ValueCountFrequency (%)
: 1764
66.7%
. 882
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 1764
100.0%
Space Separator
ValueCountFrequency (%)
882
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22932
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12566
54.8%
- 1764
 
7.7%
: 1764
 
7.7%
1 1623
 
7.1%
2 1374
 
6.0%
882
 
3.8%
. 882
 
3.8%
5 525
 
2.3%
4 476
 
2.1%
3 346
 
1.5%
Other values (4) 730
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22932
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12566
54.8%
- 1764
 
7.7%
: 1764
 
7.7%
1 1623
 
7.1%
2 1374
 
6.0%
882
 
3.8%
. 882
 
3.8%
5 525
 
2.3%
4 476
 
2.1%
3 346
 
1.5%
Other values (4) 730
 
3.2%

vr_charge_desc
Text

MISSING 

Distinct88
Distinct (%)10.0%
Missing10875
Missing (%)92.5%
Memory size92.0 KiB
2024-02-26T23:19:13.002219image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length37
Median length34
Mean length18.32993197
Min length7

Characters and Unicode

Total characters16167
Distinct characters59
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)4.4%

Sample

1st rowFelony Battery (Dom Strang)
2nd rowAggrav Battery w/Deadly Weapon
3rd rowKidnapping (Facilitate Felony)
4th rowFelony Battery
5th rowBattery
ValueCountFrequency (%)
battery 596
25.2%
aggravated 112
 
4.7%
assault 95
 
4.0%
weapon 88
 
3.7%
80
 
3.4%
felony 79
 
3.3%
robbery 72
 
3.0%
agg 57
 
2.4%
on 55
 
2.3%
officer 54
 
2.3%
Other values (143) 1074
45.5%
2024-02-26T23:19:13.203235image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 1736
 
10.7%
a 1537
 
9.5%
e 1530
 
9.5%
1487
 
9.2%
r 1349
 
8.3%
y 839
 
5.2%
B 669
 
4.1%
g 641
 
4.0%
n 597
 
3.7%
o 589
 
3.6%
Other values (49) 5193
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11824
73.1%
Uppercase Letter 2349
 
14.5%
Space Separator 1487
 
9.2%
Other Punctuation 323
 
2.0%
Close Punctuation 58
 
0.4%
Open Punctuation 58
 
0.4%
Decimal Number 51
 
0.3%
Math Symbol 9
 
0.1%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1736
14.7%
a 1537
13.0%
e 1530
12.9%
r 1349
11.4%
y 839
 
7.1%
g 641
 
5.4%
n 597
 
5.0%
o 589
 
5.0%
l 407
 
3.4%
d 377
 
3.2%
Other values (14) 2222
18.8%
Uppercase Letter
ValueCountFrequency (%)
B 669
28.5%
A 372
15.8%
W 212
 
9.0%
D 177
 
7.5%
F 151
 
6.4%
S 120
 
5.1%
E 79
 
3.4%
O 78
 
3.3%
P 77
 
3.3%
R 74
 
3.2%
Other values (12) 340
14.5%
Decimal Number
ValueCountFrequency (%)
1 15
29.4%
2 12
23.5%
5 11
21.6%
6 10
19.6%
7 2
 
3.9%
8 1
 
2.0%
Other Punctuation
ValueCountFrequency (%)
/ 320
99.1%
. 3
 
0.9%
Space Separator
ValueCountFrequency (%)
1487
100.0%
Close Punctuation
ValueCountFrequency (%)
) 58
100.0%
Open Punctuation
ValueCountFrequency (%)
( 58
100.0%
Math Symbol
ValueCountFrequency (%)
+ 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14173
87.7%
Common 1994
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1736
12.2%
a 1537
 
10.8%
e 1530
 
10.8%
r 1349
 
9.5%
y 839
 
5.9%
B 669
 
4.7%
g 641
 
4.5%
n 597
 
4.2%
o 589
 
4.2%
l 407
 
2.9%
Other values (36) 4279
30.2%
Common
ValueCountFrequency (%)
1487
74.6%
/ 320
 
16.0%
) 58
 
2.9%
( 58
 
2.9%
1 15
 
0.8%
2 12
 
0.6%
5 11
 
0.6%
6 10
 
0.5%
+ 9
 
0.5%
- 8
 
0.4%
Other values (3) 6
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16167
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 1736
 
10.7%
a 1537
 
9.5%
e 1530
 
9.5%
1487
 
9.2%
r 1349
 
8.3%
y 839
 
5.2%
B 669
 
4.1%
g 641
 
4.0%
n 597
 
3.7%
o 589
 
3.6%
Other values (49) 5193
32.1%